@article{abdollahiUrBANUrbanBeehive2025,

title = {UrBAN: Urban Beehive Acoustics and PheNotyping Dataset},

author = {M. Abdollahi and Y. Zhu and H. R. Guimarães and N. Coallier and S. Maucourt and P. Giovenazzo and T. H. Falk},

url = {https://www.scopus.com/pages/publications/105001718795?origin=resultslist},

doi = {10.1038/s41597-025-04869-1},

issn = {20524463 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Scientific Data},

volume = {12},

number = {1},

publisher = {Nature Research},

abstract = {In this paper, we present a multimodal dataset obtained from a honey bee colony in Montréal, Quebec, Canada, spanning the years of 2021 to 2022. This apiary comprised 10 beehives, with microphones recording more than 3000 hours of high quality raw audio, and also sensors capturing temperature, and humidity. Periodic hive inspections involved monitoring colony honey bee population changes, assessing queen-related conditions, and documenting overall hive health. Additionally, health metrics, such as Varroa mite infestation rates and winter mortality assessments were recorded, offering valuable insights into factors affecting hive health status and resilience. In this study, we first outline the data collection process, sensor data description, and dataset structure. Furthermore, we demonstrate a practical application of this dataset by extracting various features from the raw audio to predict colony population using the number of frames of bees as a proxy. © The Author(s) 2025.},

keywords = {Acoustics, animal, Animals, bee, Bees, Phenotype, Quebec},

pubstate = {published},

tppubtype = {article}

}